Memory
18 practitioners working with Memory:
Agent Memory Systems
How AI agents implement memory: short-term context, long-term storage, vector retrieval, and the architecture that ties it together.
AI Memory Compression
Techniques for compressing AI observations into retrievable semantic summaries that fit in context windows
Build a Personal Knowledge Graph
Connect your notes, projects, and contacts in a queryable graph. Entity extraction, Neo4j setup, and Obsidian graph visualization.
Building a Memory System
Build persistent AI memory with Claude Code using episodic-memory MCP, memory files, and external tools like Obsidian and Notion
Building an AI Second Brain
Transform AI from chatbot to persistent knowledge partner
Charles Packer — Building Machines That Learn and Remember
Context Rot: When More Tokens Mean Worse Results
LLM performance degrades predictably as context windows fill up. Learn why this happens, how to detect it, and practical strategies to maintain output quality.
Context Window Management
Keep your AI sharp by managing what fits in its working memory
Episodic Memory for LLM Agents
Give AI agents memory of specific past events with temporal context. The missing piece between semantic facts and procedural rules in the CoALA framework.
Graph Memory for Personal AI
Knowledge graphs track relationships between people, projects, and time that vector databases miss. Build AI memory that understands context across sessions.
Hybrid Retrieval: When RAG Meets Long Context
Combine RAG retrieval with long-context windows strategically instead of treating them as competing approaches
Late Chunking: Context-Aware Document Splitting for Better Retrieval
Process entire documents through embedding models before splitting to preserve cross-chunk context that traditional chunking destroys
Louis Beaumont
Founder of Mediar AI and creator of screenpipe — 24/7 local screen and audio capture for AI memory. Building the open-source Rewind alternative.
Memory Attribution and Provenance
Track where AI memories came from, when they were created, and how much to trust them
Memory Consolidation and Forgetting
How AI agents consolidate short-term observations into long-term storage using sleep-inspired patterns, plus when and what to forget.
Preference Learning: AI That Adapts to You
How AI systems infer your preferences from interactions and adapt without configuration. Covers POPI, Mem0, LaMP benchmarks, and building preference-aware systems.
Proactive AI Agent with Semantic Memory
Turn your AI from reactive chatbot into proactive assistant using heartbeats, semantic search, and persistent memory.
Self-Updating Instructions (Procedural Memory)
Build AI agents that modify their own operating instructions based on experience, feedback, and observed failures